# PFSP-TC Benchmark Datasets

## 1. Overview

This repository provides benchmark datasets for the **Permutation Flow Shop Problem with Transportation Constraints (PFSP-TC)**.  
These datasets support reproducible research and enable comparative evaluation of exact, metaheuristic, and simheuristic approaches for flow shop scheduling with integrated transportation constraints.

The data generation procedure is based on the classical **Taillard (1993)** benchmark instances and the methodology proposed by **Ahmadi et al. (2015)** for incorporating transportation-related times.

---

## 2. Dataset Content

The dataset includes three types of time-related data:

* **Processing times** – time required for each job on each machine.  
* **Transportation times** – time for moving a job from one machine to the next.  
* **Empty moving times** – time for an AGV to move without carrying a job.

All files are provided in **CSV format**.

### Folder Structure

* `processing_times/` – contains **processing time CSV files for small instances**.  
* `transportation_times/` – contains **transportation time CSV files for small instances and large instances with(small, medium, and large times)**.  
* `empty_moving_times/` – contains **empty moving time CSV files for small instances and large instances with(small, medium, and large times)**.  

---

## 3. Data Format and How to Read

All CSV files follow a **long-format structure**, where each row represents a single operation or movement.

### 3.1 Processing Times

**Columns:**

| Column | Description |
|--------|-------------|
| `instance` | Problem instance number |
| `job`      | Job index |
| `machine`  | Machine index |
| `processing_time` | Processing time of the job on the machine |


**Example:**
1,1,1,19
1,1,2,45

- Row `1,1,1,19` → Instance 1, Job 1, Machine 1, Processing Time = 19  
- Row `1,1,2,45` → Instance 1, Job 1, Machine 2, Processing Time = 45  

### 3.1 Transportation Times

CSV columns:

| Column | Description |
|--------|-------------|
| `instance` | Problem instance number |
| `from_machine`      | Source machine index |
| `to_machine`  | Destination machine index |
| `transport_time` | Transportation time for a job |

Example (small instances):

instance,from_machine,to_machine,transport_time
1,1,2,4
1,2,3,5


Example (large instances, Taillard-based):

Taill01_20_5,1,2,16
Taill01_20_5,2,3,38


Explanation:

Row 1,1,2,4 → For instance 1, moving a job from machine 1 → 2 takes 4 units of time.

Row Taill01_20_5,1,2,16 → For Taillard instance 1_20_5, moving a job from machine 1 → 2 takes 16 units of time.
### 3.1 Empty moving Times

| Column | Description |
|--------|-------------|
| `instance` | Problem instance number |
| `from_machine`      | Source machine index |
| `to_machine`  | Destination machine index |
| `empty_moving_time` | Time for an AGV to move empty |

Example (small instances):

instance,from_machine,to_machine,empty_moving_time
1,1,2,1
1,2,1,1


Example (large instances, Taillard-based):

Taill01_20_5,1,2,24
Taill01_20_5,1,3,22


Explanation:

Row 1,1,2,1 → For instance 1, an empty AGV moves from machine 1 → 2 in 1 unit of time.

Row Taill01_20_5,1,2,24 → For Taillard instance 1_20_5, an empty AGV moves from machine 1 → 2 in 24 units of time.

Important Note on AGVs

The number of Automated Guided Vehicles (AGVs) is not included in the dataset files.

The dataset provides fixed time-related parameters only.

AGVs are treated as a scenario parameter.

You can create multiple experimental scenarios by varying the number of AGVs while keeping all times unchanged.

Recommendations:

Small-sized instances: choose AGVs from {1, 2, 3}

Medium/large instances: choose AGVs randomly from 1–32

Use the same processing, transportation, and empty moving times across different AGV scenarios to ensure reproducibility.

5. Usage and Reproducibility

The dataset enables:

Reproducible experimentation for PFSP-TC.

Fair comparison between algorithms under identical time conditions.

Extensions to deterministic or stochastic scheduling models.

6. Citation

Please cite the dataset DOI provided by Harvard Dataverse.
7. Contact

For questions or clarifications:

Khadidja Bakdi
University of Tlemcen
Email: khadidja.bakdi@univ-tlemcen.dz


